Quant Researcher Intern

**Quant Researcher Intern - Systematic Commodities Hedge Fund**:Moreton Capital Partners is seeking a talented Quant Researcher Intern to help build the next generation of alpha signals in commodity futures. Our research is grounded in advanced machine learning, robust testing frameworks, and a deep understanding of global commodity markets.This role is central to our mission: you’ll take ownership of designing, testing, and refining predictive models that directly feed into live trading portfolios.**Key Responsibilities**:- Research, prototype, and validate systematic trading signals across commodities using advanced ML methods.- Design and implement rigorous backtests with realistic frictions, walk-forward validation, and robust statistical tests.- Engineer and evaluate novel features from prices, fundamentals, positioning, options data, and alternative datasets (e.g., satellite, weather and global commodity cash pricing).- Blend multiple alpha forecasts into meta-models and portfolio signals, leveraging ensemble and Bayesian methods.- Develop portfolio construction and optimization techniques and analysis tools to be able to enhance performance and track effects on portfolio execution.- Collaborate with developers to transition research into production-ready strategies.Monitor live performance, attribution, and model drift, ensuring continual improvement of the alpha library.Bonus points for:- Knowledge of commodities (agriculture, energy, metals) or macro markets.- Experience with feature engineering on non-traditional datasets (options positioning, weather, satellite).- Experience collaborating in version control environments.- Familiarity with portfolio optimisation, risk parity, or Bayesian model averaging.- Publications, Kaggle competitions, or research track record demonstrating applied ML excellence.**Requirements**:- Bachelors degree in either Statistics, Economics, Computer Science.- Strong background in machine learning and statistical modelling (tree-based models, regularisation, time-series ML).- Proficiency in Python (pandas, NumPy, scikit-learn, XGboost, PyTorch/TensorFlow).- Understanding of time-series forecasting, cross-validation techniques, and avoiding look-ahead bias.- Academic experience in research and proven ability to translate academic work to production code.- Prior exposure to systematic trading or financial modelling.- Ability to design experiments, interpret results, and iterate quickly in a research environment.**Benefits**- Research-first culture: We value deep thinking, novel approaches, and systematic rigor.- Direct exposure: Work alongside the CIO and senior researchers, with a direct line to decision-making.- Learning curve: Deep exposure to commodity markets, ML research workflows, and institutional-grade trading systems.- Close collaboration: Work alongside the CIO, Head of Quant Research, and Developers in a lean, highly motivated team.